Job Description

Applications are invited for the following full-time position in the Saw Swee Hock School of Public Health:

Research Assistant

We are looking for research assistants with a quantitative background for ongoing research in Public Health.

They will be working within the team under the Principal Investigator Assistant Professor Akira Endo alongside multiple collaborators and experts.

Methods include renewal process, network transmission modelling, branching process, Bayesian inference, particularly in the context of epidemiology and dynamics of respiratory and/or sexually-transmitted infections.

Candidates need to be able to understand infectious disease dynamic modelling, statistical modelling, have a sufficient epidemiological, mathematical and data science backgrounds, and be fluent in R programming. We will also appreciate candidates who have extensive C++, Python or Julia coding knowledge.

The candidate will be working with the Principal Investigator(s) on the analysis of large-scale behaviour and disease data, build up mathematical models of disease transmission including network science or branching process approaches.

The Principal Investigator(s) is seeking for an independent worker who is well-organized, analytical and codes competently. They will however be receiving support from a team of mathematicians, epidemiologists and statisticians, and have a diverse portfolio of tasks. Under the team’s guidance, they will be expected to co-lead their own publications.

We welcome academic creativity and will be highly supportive of candidates who wish to either pursue academia or desire for career progression provided they show self-motivation to showcase their problem-solving abilities.

Responsibilities:
 Infectious disease modelling
 Statistical analyses
 Stochastic processes
 Academic writing and publication of results
 Preparation of meeting materials for stakeholders

Requirements:
 Completed an MSc in a quantitative discipline (statistics, mathematics, computational biology, data science).
 Extensive experiences in public health research and experience in leading at least one infectious disease modelling project
 At least one (preferably more) academic publication as a main author (first/corresponding/last) in disciplines relevant to infectious disease modelling
 Strong programming skills (at least one of R/Python/Julia)
 Statistical competence (understands and can perform likelihood-based inference, ideally including Bayesian)

Please email the PI at aendo@nus.edu.sg for further details and to arrange an interview.

More Information

Location: Kent Ridge Campus

Organization: Saw Swee Hock School of Public Health

Department : Saw Swee Hock School of Public Health

Employee Referral Eligible: No

Job requisition ID : 30569